• Journal of the Institute of Convergence Signal Processing
• /
• v.24 no.1
• /
• pp.42-50
• /
• 2023

The development of technology has achieved digital innovation throughout the media industry, including production techniques and editing technologies, and has brought diversity in the form of consumer viewing through the OTT service and streaming era. The convergence of big data and deep learning networks automatically generated text in format such as news articles, novels, and scripts, but there were insufficient studies that reflected the author's intention and generated story with contextually smooth. In this paper, we describe the flow of pictures in the storyboard with image caption generation techniques, and the automatic generation of story-tailored scenarios through language models. Image caption using CNN and Attention Mechanism, we generate sentences describing pictures on the storyboard, and input the generated sentences into the artificial intelligence natural language processing model KoGPT-2 in order to automatically generate scenarios that meet the planning intention. Through this paper, the author's intention and story customized scenarios are created in large quantities to alleviate the pain of content creation, and artificial intelligence participates in the overall process of digital content production to activate media intelligence.

• KIPS Transactions on Computer and Communication Systems
• /
• v.12 no.12
• /
• pp.363-370
• /
• 2023

Object detection technology that accurately recognizes the road and surrounding conditions is a key technology in the field of autonomous driving. In the field of autonomous driving, object detection technology requires real-time performance as well as accuracy of inference services. Task offloading technology should be utilized to apply object detection technology for accuracy and real-time on resource-constrained devices rather than high-performance machines. In this paper, experiments such as performance comparison of task offloading, performance comparison according to input image resolution, and performance comparison according to camera object resolution were conducted and the results were analyzed in relation to the application of task offloading for real-time object detection of autonomous driving in resource-constrained devices. In this experiment, the low-resolution image could derive performance improvement through the application of the task offloading structure, which met the real-time requirements of autonomous driving. The high-resolution image did not meet the real-time requirements for autonomous driving due to the increase in communication time, although there was an improvement in performance. Through these experiments, it was confirmed that object recognition in autonomous driving affects various conditions such as input images and communication environments along with the object recognition model used.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.27 no.5
• /
• pp.113-119
• /
• 2023

Most infrastructure structures were completed during periods of economic growth. The number of infrastructure structures reaching their lifespan is increasing, and the proportion of old structures is gradually increasing. The functions and performance of these structures at the time of design may deteriorate and may even lead to safety accidents. To prevent this repercussion, accurate inspection and appropriate repair are requisite. To this end, demand is increasing for computer vision and deep learning technology to accurately detect even minute cracks. However, deep learning algorithms require a large number of training data. In particular, label images indicating the location of cracks in the image are required. To secure a large number of those label images, a lot of labor and time are consumed. To reduce these costs as well as increase detection accuracy, this study proposed a learning structure based on mean teacher method. This learning structure was trained on a dataset of 900 labeled image dataset and 3000 unlabeled image dataset. The crack detection network model was evaluated on over 300 labeled image dataset, and the detection accuracy recorded a mean intersection over union of 89.23% and an F1 score of 89.12%. Through this experiment, it was confirmed that detection performance was improved compared to supervised learning. It is expected that this proposed method will be used in the future to reduce the cost required to secure label images.

• Journal of Industrial Convergence
• /
• v.21 no.9
• /
• pp.41-48
• /
• 2023

Recently, there has been a sharp increase in the damages caused by ransomware across various sectors of society, including individuals, businesses, and nations. Ransomware is a malicious software that infiltrates user computer systems, encrypts important files, and demands a ransom in exchange for restoring access to the files. Due to its diverse and sophisticated attack techniques, ransomware is more challenging to detect than other types of malware, and its impact is significant. Therefore, there is a critical need for accurate detection and mitigation methods. To achieve precise ransomware detection, an inference engine of a detection system must possess knowledge of ransomware features. In this paper, we propose a model to extract and classify the characteristics of ransomware for accurate detection of ransomware, calculate the similarity of the extracted characteristics, reduce the dimension of the characteristics, group the reduced characteristics, and classify the characteristics of ransomware into attack tools, inflow paths, installation files, command and control, executable files, acquisition rights, circumvention techniques, collected information, leakage techniques, and state changes of the target system. The classified characteristics were applied to the existing ransomware to prove the validity of the classification, and later, if the inference engine learned using this classification technique is installed in the detection system, most of the newly emerging and variant ransomware can be detected.

• Advances in nano research
• /
• v.16 no.4
• /
• pp.375-394
• /
• 2024

The extensive utilization of concrete has given rise to environmental concerns, specifically concerning the depletion of river sand. To address this issue, waste deposits can provide manufactured-sand (MS) as a substitute for river sand. The objective of this study is to explore the application of machine learning techniques to facilitate the production of manufactured-sand concrete (MSC) containing stone nano-powder through estimating the splitting tensile strength (STS) containing compressive strength of cement (CSC), tensile strength of cement (TSC), curing age (CA), maximum size of the crushed stone (Dmax), stone nano-powder content (SNC), fineness modulus of sand (FMS), water to cement ratio (W/C), sand ratio (SR), and slump (S). To achieve this goal, a total of 310 data points, encompassing nine influential factors affecting the mechanical properties of MSC, are collected through laboratory tests. Subsequently, the gathered dataset is divided into two subsets, one for training and the other for testing; comprising 90% (280 samples) and 10% (30 samples) of the total data, respectively. By employing the generated dataset, novel models were developed for evaluating the STS of MSC in relation to the nine input features. The analysis results revealed significant correlations between the CSC and the curing age CA with STS. Moreover, when delving into sensitivity analysis using an empirical model, it becomes apparent that parameters such as the FMS and the W/C exert minimal influence on the STS. We employed various loss functions to gauge the effectiveness and precision of our methodologies. Impressively, the outcomes of our devised models exhibited commendable accuracy and reliability, with all models displaying an R-squared value surpassing 0.75 and loss function values approaching insignificance. To further refine the estimation of STS for engineering endeavors, we also developed a user-friendly graphical interface for our machine learning models. These proposed models present a practical alternative to laborious, expensive, and complex laboratory techniques, thereby simplifying the production of mortar specimens.

• Journal of Internet of Things and Convergence
• /
• v.10 no.2
• /
• pp.137-148
• /
• 2024

The purpose of this study is to determine how the use of retailtech technology affects consumers' purchase intention. Furthermore, this study aims to investigate the mediating effects of technology usefulness and ease of use on this influence relationship and whether experiential marketing moderates consumers' purchase intention. The survey was conducted from August 1, 2023 to September 30, 2023, and a total of 257 people participated in the study. For statistical analysis, hierarchical regression analysis, three-stage mediation regression analysis, and hierarchical three-stage controlled regression analysis were conducted to test the hypothesis. The results of the study are as follows. First, it was confirmed that big data-AI utilization, mobile-SNS utilization, live commerce utilization, and IoT utilization affect purchase intention in retail technology utilization. Second, technology usefulness has a mediating effect on IoT utilization, mobile-SNS utilization, and big data-AI utilization. Third, perceived ease of use of technology mediated the effects of IoT utilization, mobile-SNS utilization, live-commerce utilization, and big data-AI utilization. Fourth, escapist experience has a moderating effect on mobile SNS utilization and live commerce utilization. Fifth, esthetic experience has a moderating effect on mobile-SNS utilization and big data-AI utilization. Through this study, we hope that the domestic distribution industry will contribute to national competitiveness by securing the competitive advantage of companies by utilizing new technologies in entering the global market.

• Information Systems Review
• /
• v.21 no.4
• /
• pp.175-195
• /
• 2019

This study conducted both big data and netnography analysis to analyze consumer needs and behaviors of online consumer community. Big data analysis is easy to identify correlations, but causality is difficult to identify. To overcome this limitation, we used netnography analysis together. The netnography methodology is excellent for context grasping. However, there is a limit in that it is time and costly to analyze a large amount of data accumulated for a long time. Therefore, in this study, we searched for patterns of overall data through big data analysis and discovered outliers that require netnography analysis, and then performed netnography analysis only before and after outliers. As a result of analysis, the cause of the phenomenon shown through big data analysis could be explained through netnography analysis. In addition, it was able to identify the internal structural changes of the community, which are not easily revealed by big data analysis. Therefore, this study was able to effectively explain much of online consumer behavior that was difficult to understand as well as contextual semantics from the unstructured data missed by big data. The big data-netnography integrated model proposed in this study can be used as a good tool to discover new consumer needs in the online environment.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2007.05a
• /
• pp.1410-1413
• /
• 2007

현재 초등학교에서 실시되고 있는 ICT(Information & Communication Technology) 교육은 ICT 소양교육과 ICT 활용교육으로 구분되어 실시되고 있다. ICT 소양교육은 컴퓨터 과학을 기반으로 하여 정보기술과 통신 기술에 대한 기본적인 소양교육으로 그 내용을 구성하고 있다. 현재 초고속 통신망의 발달과 컴퓨터 사용으로 인하여 소양교육에 대한 학업 성취도는 매우 향상되어 가고 있는 실정이다. 따라서 앞으로의 ICT 교육은 활용 교육에 더 많은 노력을 기울여 전 교과에 걸쳐 정보 통신 기술을 활용하여 교육의 내용뿐만 아니라 교육의 방법 등에서 많은 변화의 필요성이 증대되고 있다. ICT 활용교육에서 컴퓨터를 활용하기 위해서는 실생활에서 습득하거나 측정된 데이터를 컴퓨터에 입력하는 과정, 데이터 로깅(data logging), 으로부터 시작 한다. 최근의 유비쿼터스 컴퓨팅 환경에서 데이터가 발생한 곳에서 즉시 받아들이고 또한 결과가 필요한 곳에서 즉시 정보를 제공하여 주는 컴퓨팅 환경을 구성하는 것이 매우 중요 하다고 본다. 이에 본 연구에서는 유비쿼터스 컴퓨팅의 기본 개념 중 하나인 실시간 데이터 로깅 기법을 응용하여 초등학교에서 ICT 활용 학습 활동 시 발생되는 각종 원시 데이터들을 컴퓨터로 가져오는 데이터로깅 모델을 제안하고, 초등학교 과학과를 중심으로 교육과정의 실험 요소들을 분석하여 이를 개발된 모델에 적용하였다. 데이터 로깅 모델 적용 결과, 손쉽게 해당 원시 데이터를 수집할 수 있었고 데이터의 처리 및 분석을 간편하게 수행하여 정확한 실험 데이터를 바탕으로 실험 결과에 대한 토의, 토론에 더욱 많은 시간을 할애할 수 있었으며 학교에서의 ICT 활용 교육의 새로운 모델을 제시하였다.다.ovoids에서도 각각의 점들에 대한 선량을 측정하였다. SAS와 SSAS의 직장에 미치는 선량차이는 실제 임상에서의 관심 점들과 가장 가까운 25 mm(R2)와 30 mm(R3)거리에서 각각 8.0% 6.0%였고 SAS와 FWAS의 직장에 미치는 선량차이는 25 mm(R2) 와 30 mm(R3)거리에서 각각 25.0% 23.0%로 나타났다. SAS와 SSAS의 방광에 미치는 선량차이는 20 m(Bl)와 30 mm(B2)거리에서 각각 8.0% 3.0%였고 SAS와 FWAS의 방광에 미치는 선량차이는 20 mm(Bl)와 30 mm(B2)거리에서 각각 23.0%, 17.0%로 나타났다. SAS를 SSAS나 FWAS로 대체하였을 때 직장에 미치는 선량은 SSAS는 최대 8.0 %, FWAS는 최대 26.0 %까지 감소되고 방광에 미치는 선량은 SSAS는 최대 8.0 % FWAS는 최대 23.0%까지 감소됨을 알 수 있었고 FWAS가 SSAS 보다 차폐효과가 더 좋은 것으로 나타났으며 이 두 종류의 shielded applicator set는 부인암의 근접치료시 직장과 방광으로 가는 선량을 감소시켜 환자치료의 최적화를 이룰 수 있을 것으로 생각된다.)한 항균(抗菌) 효과(效果)를 나타내었다. 이상(以上)의 결과(結果)로 보아 선방활명음(仙方活命飮)의 항균(抗菌) 효능(效能)은 군약(君藥)인 대황(大黃)의 성분(成分) 중(中)의 하나인 stilbene 계열(系列)의 화합물(化合物)인 Rhapontigenin과 Rhaponticin의 작용(作用)에 의(依)한 것이며, 이는 한의학(韓醫學) 방제(方劑) 원리(原理)인 군신좌사(君臣佐使) 이론(理論)에서 군약(君藥)이 주증(主症)에 주(主)로 작용(作用)하는 약물(藥物)이라는 것을 밝혀주는 것

• Asia pacific journal of information systems
• /
• v.19 no.3
• /
• pp.69-98
• /
• 2009

We investigated what factors motivate gamers to participate in a guild and why they continue to be engaged as members of the guild. We find that, based on the result of focus group interviews with MMORPG gamers, social support and self-esteem factors play important roles. Considering both prior research and the focus group interviews we have conducted, we define social support and character control as independent variables. Character identity, guild identity, and self-esteem are proposed as mediating variables while guild flow and game loyalty as dependent variables. Accordingly, we develop the research model and hypotheses, and verify them empirically. Based on our experiences of playing the WoW game, we proposed a research model and conducted focus-group interviews (FGIs). FGIs involve formulating a hypothesis and then collecting some relevant data. FGIs were conducted face-to-face with students of C University in Korea. We formulated structured interview schedules, and the questions were based on our research variables and personal experiences. The questions for the interviews encompassed the following areas: (a) the demographic characteristics of the focus group; (b) the number of years for which respondents had played online games; (c) the motive for starting a game; (d) the number of game-characters assumed by each gamer; (e) the type of game played; and (f) other issues such as the reasons for involvement in the play, the willingness to reuse the game in case new versions were released, etc. On average, it took two hours to interview each of three groups. A primary set of FGIs was conducted with three groups on the premise that there would be some differences caused by character race (Horde vs. Alliance) or by playable server (Normal vs. Combat). With respect to the manner of playing, we found that guild members shared information, felt a sense of belonging, and played computer games for quite a long time through the guild; however, they did not undergo these experiences when playing alone. Gamers who belonged to a specific guild helped other players without expecting compensation for that, freely shared information about the game, gave away items for free, and more generous with other members who made mistakes. The guild members were aware of the existence other members and experienced a sense of belonging through interactions with, and evaluations from, other players. It was clear that social support was shown within the guild and that it played an important role as a major research variable. Based on the results of the first FGIs, a second set of in-depth FGIs was carried out with a focus on the psychology of the individual within the guild and the social community of the guild. The second set of FGIs also focused on the guild's offline meetings. Gamers, over all, recognize the necessity of joining a community, not only off-line but also online world of the guild. They admit that the guild is important for them to easily and conveniently enjoy playing online computer games. The active behavior and positive attitudes of existing guild members can motivate new members of the guild to adapt themselves to the guild environment. They then adopt the same behaviors and attitudes of established guild members. In this manner, the new members of the guild strengthen the bonds with other gamers while feeling a sense of belonging, and developing social identity, thereby. It was discovered that the interaction among guild members and the social support encouraged new gamers to quickly develop a sense of social identity and increase their self-esteem. The guild seemed to play the role of socializing gamers. Sometimes, even in the real world, the guild members helped one another; therefore, the features of the guild also spilled over to the offline environment. We intend to use self-esteem, which was found through the second set of FGIs, as an important research variable. To collect data, an online survey was designed with a questionnaire to be completed by WoW gamers, who belong to a guild. The survey was registered on the best three domestic game-sites: 'WoW playforum,' 'WoW gamemeca,' and 'Wow invent.' The selected items to be measured in the questionnaire were decided based on prior research and data from FGIs. To verify the content of the questionnaire, we carried out a pilot test with the same participants to point out ambiguous questions as a way to ensure maximum accuracy of the survey result. A total of 244 responses were analyzed from the 250 completed questionnaires. The SEM analysis was used to test goodness-of-fit of the model. As a result, we found important results as follows: First, according to the statistics, social support had statistically significant impacts on character control, character identity, guild identity and self-esteem. Second, character control had significant effects on character identity, guild identity and self-esteem. Third, character identity shows its clear impact on self-esteem and game loyalty. Fourth, guild identity affected self-esteem, guild flow and game loyalty. Fifth, self-esteem had a positive influence on the guild flow. These days, the number of virtual community is rising along with its significance largely because of the nature of the online games. Accordingly, this study is designed to clarify the psychological relationship between gamers within the guild that has been generally established by gamers to play online games together. This study focuses on the relationships in which social support influences guild flow or game loyalty through character control, character identity, guild identity, and self-esteem, which are present within a guild in the MMORPG game environment. The study results are as follows. First, the effects of social support on character control, character identity, guild identity and self-esteem are proven to be statistically significant. It was found that character control improves character identity, guild identity and self-esteem. Among the seven variables, social support, which is derived from FGIs, plays an important role in this study. With the active support of other guild members, gamers can improve their ability to develop good characters and to control them. Second, character identity has a positive effect on self-esteem and game loyalty, while guild identity has a significant effect on self-esteem, guild flow and game loyalty. Self-esteem affects guild flow. It was found that the higher the character and guild identities become, the greater the self-esteem is established. Contrary to the findings of prior research, our study results indicate that the relationship between character identity and guild flow is not significant. Rather, it was found that character identity directly affects game players' loyalty. Even though the character identity had no direct effect on increasing guild flow, it has indirectly affected guild flow through self-esteem. The significant relationship between self-esteem and guild flow indicates that gamers achieve flow, i.e., a feeling of pleasure and excitement through social support. Several important implications of this study should be noted. First, both qualitative and quantitative methods were used to conduct this study. Through FGIs, it was observed that both social support and self-esteem are important variables. Second, because guilds had been rarely studied, this research is expected to play an important role in the online community. Third, according to the result, six hypotheses (H1, H5, H6, H7, H8, and H11) setup based on FGIs, were statistically significant; thus, we can suggest the corresponding relationships among the variables as a guideline for follow-up research. Our research is significant as it has following implications: first, the social support of the guild members is important when establishing character control, character identity, guildidentity and self-esteem. It is also a major variable that affects guild flow and game loyalty. Second, character control when improved by social support shows notable influence on the development of character identity, guild identity and self-esteem. Third, character identity and guild identity are major factors to help establish gamers' own self-esteem. Fourth, character identity affects guild flow through self-esteem and game loyalty. The gamers usually express themselves through characters; the higher character identity is, the more loyalty a gamer has. Fifth, guild identity, established within the guild, has clear effects on self-esteem, guild flow and game loyalty. Sixth, qualitative and quantitative methods are employed to conduct this study. Based on the results of focus group interviews and SEM analysis, we find that the social support by guild members and psychological factors are significant in strengthening the flow of guild and loyalty to the game. As such, game developers should provide some extra functions for guild community, through which gamers can play online games in collaboration with one another. Also, we suggest that positive self-esteem which is built up through social support can help gamers achieve higher level of flow and satisfaction, which will consequently contribute to minimizing the possibility for the players to develop negative attitude toward the guild they belong to.

• The korean journal of orthodontics
• /
• v.36 no.1 s.114
• /
• pp.14-29
• /
• 2006

Developments in computer technology have made possible the 3-dimensional (3-D) evaluation of hard and soft tissues in orthodontic diagnosis, treatment planning and post-treatment results. In this study, Korean adults with normal occlusion (male 30, female 30) were scanned by a 3-D laser scanner, then 3-D facial images formed by the Rapidform 2004 program (Inus Technology Inc., Seoul, Korea.). Reference planes in the facial soft tissue 3-D images were established and a 3-D coordinate system (X axis-left/right, Y axis-superior/inferior, Z axis-anterior/posterior) was established by using the soft tissue nasion as the zero point. Twenty-nine measurement points were established on the 3-D image and 43 linear measurements, 8 angular measurements, 29 linear distance ratios were obtained. The results are as follows; there were significant differences between males and females in the nasofrontal angle $(male:\;142^{\circ},\;female:\;147^{\circ})$ and transverse nasal prominence $(male:\;112^{\circ},\;female:\;116^{\circ})$ (p<0.05). The transverse upper lip prominence was $107^{\circ}$ in males, $106^{\circ}$ in females and the transverse mandibular prominence was $76^{\circ}$ in both males and females. Li-Me' was 0.4 times the length of Go-Me'(mandibular body length) and the mouth height was also 0.4 times the width of the mouth width. The linear distance ratio from the coronal reference plane of FT, Zy, Pn, ULPm, Li, Me' was -1/-1/1/0.5/0.5/-0.6 respectively. The 3-D facial model of Korean adults with normal occlusion were be constructed using coordinate values and linear measurement values. These data may be used as a reference in 3-D diagnosis and treatment planning for malocclusion and dentofacial deformity patients and applied for 3-D analysis of facial soft tissue changes before and after orthodontic treatment and orthognathic surgery.